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Optimization of Media:
Criteria for optimization of media
The process of optimization of media is done before the media preparation toget maximum yield at industrial level.
It should be target oriented means either for biomass production or for desireproduction.
When considering the biomass growth phase in isolation it must berecognized that efficiently grown biomass produced by an 'optimized' highproductivity growth phase is not necessarily best suited for its ultimatepurpose, such as synthesizing the desired product.
The optimization of a medium such that it meets as many as possible of thefollowing seven criteria:
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Methods of optimization of media
Classical Method
The Plackett-Burman design
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Classical Method
Medium optimization by the classical method
involve changing one independent variable
(nutrient, antifoam, pH, temperature, etc.)while fixing all the others at certain level
can be extremely time consuming
expensive for a large number of variables.
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Each possible combination of independent
variable at appropriate levels could require a
large number of experiments, xnwherex is the
number of levels and n is the number of
variables.
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This may be quite appropriate for three
nutrients at two concentrations (2 3 trials) but
not for six nutrients at three concentrations.In
this instance 36 (729) trials would be needed.
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Industrially the aim is to perform the
minimum number of experiments to
determine optimal conditions. Other
alternative strategies must therefore be
considered which allow more than one
variable to be changed at a time
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The Plackett-Burman design
When more than five independent variables are
to be investigated, the Plackett-Burman design
may be used to find the most important
variables in a system, which are then
optimized in further studies.
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This technique allows for the evaluation of
X-I variables by X experiments. X must be a
multiple of 4, e.g. 8, 12, 16, 20, 24, etc.
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Normally one determines how many experimental
variables need to be included in an investigationand then selects the Plackett-Burman design
which meets that requirement most closely in
multiples of 4.
Any factors not assigned to a variable can be
designated as a dummy variable. And factors
known to not have any effect may be included
and designated as dummy variables
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Table 4.16 shows a Plackett-Burman design for seven
variables (A -G) at high and low levels in which two
factors, E and G, are designated as 'dummy' variables.
From Principles of Fermentation
Technology,- Peter F. Stanbury,
Allen Whitaker, Stephen J. Hall,Second Edition,
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Each horizontal row represents a trial and each
vertical column represents the H (high) and L
(low) values of one variable in all the trials. The effects of the dummy variables are calculated
in the same way as the effects of the
experimental variables.
If there are no interactions and no errors in
measuring the response, the effect shown by a
dummy variable should be O.
If the effect is not equal to 0, it is assumed to be ameasure of the lack of experimental precision
plus any analytical error in measuring the
Response.
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From Principles of Fermentation
Technology,- Peter F. Stanbury,
Allen Whitaker, Stephen J. Hall,Second Edition,
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The stages in analysing the data (Tables 4.16 and
4.17) using Nelson's (1982) example are as follows:
1. Determine the difference between theaverage of the H (high) and L (low)
responses for each independent and
dummy variable.
Therefore the difference =LA (H) - LA (L).
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The effect of an independent variable on the
response is the difference between the averageresponse for the four experiments at the highlevel and the average value for four experimentsat the low level.
Thus the effect of
This value would be near zero for the dummy variables
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2. Estimate the mean square of each variable (the variance of effect).
ForA the mean square will be =
3. The experimental error can be calculated by averaging the mean squares of the dummy
effects of E and G.
Thus, the mean square for error =
This experimental error is not significant.
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4. The final stage is to identify the factors which are showing large effects. In the example
this was done using an F-test for
Factor mean square.
error mean square.
When Probability Tables are examined it is found that
Factors A, Band F show large effects which are very
significant, whereas C shows a very low effect which
is not significant and D shows no effect. A, B and F
have been identified as the most important factors.